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Data Engineering with dbt

You're reading from   Data Engineering with dbt A practical guide to building a cloud-based, pragmatic, and dependable data platform with SQL

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781803246284
Length 578 pages
Edition 1st Edition
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Author (1):
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Roberto Zagni Roberto Zagni
Author Profile Icon Roberto Zagni
Roberto Zagni
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Table of Contents (21) Chapters Close

Preface 1. Part 1: The Foundations of Data Engineering
2. Chapter 1: The Basics of SQL to Transform Data FREE CHAPTER 3. Chapter 2: Setting Up Your dbt Cloud Development Environment 4. Chapter 3: Data Modeling for Data Engineering 5. Chapter 4: Analytics Engineering as the New Core of Data Engineering 6. Chapter 5: Transforming Data with dbt 7. Part 2: Agile Data Engineering with dbt
8. Chapter 6: Writing Maintainable Code 9. Chapter 7: Working with Dimensional Data 10. Chapter 8: Delivering Consistency in Your Data 11. Chapter 9: Delivering Reliability in Your Data 12. Chapter 10: Agile Development 13. Chapter 11: Team Collaboration 14. Part 3: Hands-On Best Practices for Simple, Future-Proof Data Platforms
15. Chapter 12: Deployment, Execution, and Documentation Automation 16. Chapter 13: Moving Beyond the Basics 17. Chapter 14: Enhancing Software Quality 18. Chapter 15: Patterns for Frequent Use Cases 19. Index 20. Other Books You May Enjoy

Building the STG model for the first dimension

We have instructed dbt to load our CSV data in a table and we can use that as we would any other model through the ref(…) function.

That would work, but when we consider the CSV under the seed folder could be a temporary solution only, like in this case, we prefer to take the data loaded as a seed in use through sources, as we do with any external data.

Defining the external data source for seeds

We will define an external data source to read our seeds in a metadata-driven way so that we can easily adapt if the data would stop coming from a seed and start to come from a different place, such as a table from a master data system or a file from a data lake.

Let’s define a YAML file to define the sources for the seeds, and then add the config for the seed that we have just created:

  1. Create a new file named source_seed.yml in the models folder.
  2. Add the configuration for the seed external source, as in the...
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